1. Field of the Invention
This invention relates to a control system, and in particular to a control system with real-time adaptation carried out by adaptive control such as an auxiliary neural network controller.
2. Discussion of Related Art
Conventional control systems are linear and time-invariant. For each input, they are designed to yield a proportional response. However, many systems (hereinafter referred to as plants) controlled by such controllers are themselves non-linear and time-variant and therefore even the best models of plant responses are subject to uncertainty. As a result, in order to achieve a stable response, the range of possible inputs must be limited to those which will keep plant outputs well within a predicted response envelope. In practice, linear time-invariant control systems are designed for specific operating points. Stability margins are built-in so that the responses do not fall outside of an operating envelope due to modeling uncertainties, which often means the plant is not commanded to move as quickly as it can. Thus, in conventional linear control system design, there is an inherent tradeoff between performance and robustness. The more robust or conservative the design, the more limited the performance of the system.
Adaptive control uses feedback to compensate for unpredictability in control system models by providing for on-line parameter identification and necessary changes in the control responses or gains. The payoff is enhanced performance, but it comes at the expense of a higher number of computations in the feedback system and more importantly a lack of guarantee for stability. Conventional adaptive control designs, including those which propose to use neural networks to identify and adapt to changing plant parameters, are subject to unmodeled high-frequency dynamics and unmeasurable output disturbances, which can lead to unbounded adaptation and eventual instability. As a result of concerns over stability, and a lack of measure of an adequate stability, true adaptive control systems have yet to be widely accepted in practice.
Neural network-based control systems are useful because neural networks can represent any arbitrary nonlinear functions and can learn from examples the model of the plant, therefore allowing them to control plants with highly complex dynamics. However, if a neural-network controller can change the feedback characteristics online, then it becomes an adaptive control and therefore may suffer from the same problems as an adaptive control.
Three genetic configurations for a neural network controller can be identified. In the direct neuro-controller shown in FIG. 5(a), the neural network 1000 is the sole control system that directly issues the control signals needed to make the plant 1010 behave like the model 1020 in response to input from a sensor 1030. This configuration takes advantage of the internal complexity of the neural network to produce the compensation. Since the neural network does not initially know what constitutes a good control signal sequence, it requires considerable training. It is also susceptible to deviations from the training data and must be completely retrained if there are substantial changes in the plant. Moreover, there is no guarantee of stability.
In an indirect neuro-controller (FIG. 5b)), the neural network 1001 acts as a pattern classifier whose output updates the gains associated with a control system 1002 to force the plant to follow a model. Because of the explicit structure of the baseline control, both off-line training and on-line adaptation of the neural network is expected to be faster than the direct neuro-controller. However, this neuro-controller is only as powerful as the nominal control design and, therefore, does not take full advantage of the inherent complexity of the neural network. As before, there is still the issue of unbounded adaptation.
The third configuration (FIG. 5(c)) is a type of neuro-control that is particularly interesting to researchers looking at models of biological motor control due to its inclusion of a feedforward control. The function of the feedback controller 10020 is gradually taken over by a feedforward controller 10021 as the neural network learns more about controlling the plant. However, this switch may not be complete when there are too many external disturbances to attenuate with the feedback control, although it may be possible to have the feedforward portion 10020 provide some stability, with the feedback part 10021 providing the corrections for the disturbances.
After reviewing these three basic architectures, the Inventors have found that modifications must be made to each of the proposed structures to arrive at a viable design. These changes aim at fully utilizing the complexity of a neural network for pattern matching without over-taxing its capability, and at the same time providing nominal stability as well as preventing unbounded adaptation. The result is a unique design that offers all the benefits of the neural network adaptive control without compromising the safety of the plant operations.